Lance: efficient low-precision quantized winograd convolution for neural networks based on graphics processing units

Guangli Li; Lei Liu; Xueying Wang; Xiu Ma; Xiaobing Feng

ICASSP 2020accepted0 citations

Lance: efficient low-precision quantized winograd convolution for neural networks based on graphics processing units

Guangli Li, Lei Liu, Xueying Wang, Xiu Ma, Xiaobing Feng

Abstract

Accelerating deep convolutional neural networks has become an active topic and sparked an interest in academia and industry. In this paper, we propose an efficient low-precision quan-tized Winograd convolution algorithm, called LANCE, which combines the advantages of fast convolution and quantization techniques. By embedding linear quantization operations into the Winograd-domain, the fast convolution can be performed efficiently under low-precision computation on graphics processing units. We test neural network models with LANCE on representative image classification datasets, including SVHN, CIFAR, and ImageNet. The experimental results show that our 8-bit quantized Winograd convolution improves the performance by up to 2.40× over the full-precision convolution with trivial accuracy loss.

BibTeX

@inproceedings{icassp2020_lanceefficientlo,
  title = {Lance: efficient low-precision quantized winograd convolution for neural networks based on graphics processing units},
  author = {Guangli Li and Lei Liu and Xueying Wang and Xiu Ma and Xiaobing Feng},
  booktitle = {ICASSP 2020},
  year = {2020}
}